def test_load_dataset_with_oneHot(self):
galaxy_data_set_strategy = GalaxyDataSetLabelStrategy()
context = Context(galaxy_data_set_strategy)
dataset = context.load_dataset(csv_file=self.path,
one_hot=True,
validation_size=np.float32(0.2))
self.assertTrue(dataset.train._num_examples == 25091)
def test_load_dataset_with_oneHot(self):
galaxy_data_set_strategy = MusicGenreJMIRMFCCsStrategy()
context = Context(galaxy_data_set_strategy)
dataset = context.load_dataset(csv_file=self.path,
one_hot=True,
validation_size=np.float32(0.2))
self.assertTrue(dataset.train._num_examples == 52400)
class TestDataSet(TestCase):
def setUp(self):
# Test batch size.
self.batch_size = 64
# Test CSV path.
self.path = os.environ["VIRTUAL_ENV"] + "/data/csv/galaxy/galaxy.csv"
# Load data set.
galaxy_data_set_strategy = GalaxyDataSetImageStrategy()
self.context = Context(galaxy_data_set_strategy)
self.dataset = self.context.load_dataset(csv_file=self.path, one_hot=True, validation_size=np.float32(0.2))
def test_load_images(self):
# Get a batch of data.
x_batch, y_true_batch = self.dataset.train.next_image_batch(self.batch_size)
# Load images in this batch.
self.dataset.train.load_images(x_batch)
# Get the first image ID in the training data set.
test_dataset_img_name = self.dataset.train._img_names[0][0]
# Get the reference image corresponding to the first image of data set.
reference_dataset_img_name = self.dataset.train._images[0]
# Get the path of the first image ID.
path = os.environ["VIRTUAL_ENV"] + "/data/images/" + str(test_dataset_img_name) + ".jpg"
# Load image.
test_dataset_image = cv2.imread(path)
# Transform image as a 32-bit numpy array.
test_dataset_image = test_dataset_image.astype(np.float32)
# Normalize the image.
test_dataset_image = np.multiply(test_dataset_image, 1.0 / 255.0)
np.testing.assert_array_equal(reference_dataset_img_name, test_dataset_image)
def test_next_batch(self):
# First batch
x_batch1, y_true_batch1 = self.dataset.train.next_image_batch(self.batch_size)
# Second batch
x_batch2, y_true_batch2 = self.dataset.train.next_image_batch(self.batch_size)
np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, x_batch1, x_batch2)
def test_validation_size(self):
dataset = self.context.load_dataset(csv_file=self.path, one_hot=True, validation_size=np.float32(0))
self.assertTrue(dataset.train._num_examples == 31364)
self.assertRaises(Exception, lambda:
self.context.load_dataset(csv_file=self.path, one_hot=True, validation_size=np.float32(-0.1)))
def test_load_dataset_with_oneHot(self):
galaxy_data_set_strategy = GalaxyDataSetImageStrategy()
context = Context(galaxy_data_set_strategy)
dataset = context.load_dataset(csv_file=self.path,
one_hot=True,
validation_size=np.float32(0.2))
self.assertTrue(
dataset.train._num_examples == int(np.round(31364 * 0.8)))
self.assertTrue(
dataset.valid._num_examples == int(np.round(31364 * 0.2)))
def setUp(self):
# Test batch size.
self.batch_size = 64
# Test CSV path.
self.path = os.environ["VIRTUAL_ENV"] + "/data/csv/galaxy/galaxy.csv"
# Load data set.
galaxy_data_set_strategy = GalaxyDataSetImageStrategy()
self.context = Context(galaxy_data_set_strategy)
self.dataset = self.context.load_dataset(csv_file=self.path, one_hot=True, validation_size=np.float32(0.2))
def setUp(self):
validation_size = 0.2
# Get the ground truth CSV file from script's parameters.
self.galaxy_csv_file = os.environ["VIRTUAL_ENV"] + "/data/csv/galaxy/galaxy.csv"
self.galaxy_images_path = os.environ["VIRTUAL_ENV"] + "/data/images/"
# Create instance of data set loading strategies.
galaxy_label_data_set_strategy = GalaxyDataSetLabelStrategy()
# Set the context to galaxy label data set loading strategy.
context = Context(galaxy_label_data_set_strategy)
context.set_strategy(galaxy_label_data_set_strategy)
self.label_dataset = context.load_dataset(csv_file=self.galaxy_csv_file, one_hot=False,
validation_size=np.float32(validation_size))
def test_load_dataset_no_oneHot(self):
galaxy_data_set_strategy = GalaxyDataSetFeatureStrategy()
context = Context(galaxy_data_set_strategy)
dataset = context.load_dataset(csv_file=self.path, one_hot=False, validation_size=self.validation_size)
self.assertTrue(dataset.train._num_examples == 25091)
def main():
"""
Program's entry point.
"""
# The desired validation size.
validation_size = 0.2
# Get the ground truth CSV file from script's parameters.
galaxy_csv_file = os.environ["VIRTUAL_ENV"] + "/data/csv/galaxy/galaxy.csv"
galaxy_feature_csv_file = os.environ[
"VIRTUAL_ENV"] + "/data/csv/galaxy/galaxy_feature_vectors.csv"
spam_feature_csv_file = os.environ[
"VIRTUAL_ENV"] + "/data/csv/spam/spam.csv"
galaxy_images_path = os.environ["VIRTUAL_ENV"] + "/data/images/"
galaxy_feature_vector_export_path = os.environ[
"VIRTUAL_ENV"] + "/data/csv/galaxy/exported_personal_galaxy_feature_vectors.csv"
galaxy_mlp_export_path = os.environ[
"VIRTUAL_ENV"] + "/data/models/exports/MLP/my_mlp"
# Create instance of data set loading strategies.
galaxy_image_data_set_strategy = GalaxyDataSetImageStrategy()
galaxy_feature_data_set_strategy = GalaxyDataSetFeatureStrategy()
galaxy_label_data_set_strategy = GalaxyDataSetLabelStrategy()
spam_feature_dataset_strategy = SpamDataSetFeatureStrategy()
# Set the context to galaxy image data set loading strategy.
context = Context(galaxy_image_data_set_strategy)
img_dataset = context.load_dataset(
csv_file=galaxy_csv_file,
one_hot=True,
validation_size=np.float32(validation_size))
# Set the context to galaxy feature data set loading strategy.
context.set_strategy(galaxy_feature_data_set_strategy)
feature_oneHot_dataset = context.load_dataset(
csv_file=galaxy_feature_csv_file,
one_hot=True,
validation_size=np.float32(0.2))
feature_dataset = context.load_dataset(csv_file=galaxy_feature_csv_file,
one_hot=False,
validation_size=np.float32(0.2))
# Set the context to galaxy label data set loading strategy.
context.set_strategy(galaxy_label_data_set_strategy)
label_dataset = context.load_dataset(
csv_file=galaxy_csv_file,
one_hot=False,
validation_size=np.float32(validation_size))
context.set_strategy(spam_feature_dataset_strategy)
spam_feature_dataset = context.load_dataset(
csv_file=spam_feature_csv_file,
one_hot=False,
validation_size=np.float32(validation_size))
# For TP02, set the discretization strategy and discretize data.
preprocessor_context = DiscretizerContext(
SupervisedDiscretizationStrategy())
supervised_discretised_dataset = preprocessor_context.discretize(
data_set=feature_dataset, validation_size=np.float32(validation_size))
preprocessor_context.set_strategy(UnsupervisedDiscretizationStrategy())
unsupervised_discretised_dataset = preprocessor_context.discretize(
data_set=feature_dataset, validation_size=np.float32(validation_size))
# Process galaxies.
galaxy_processor = GalaxyProcessor(galaxy_images_path)
features = galaxy_processor.process_galaxy(label_dataset)
# Save extracted features to file.
np.savetxt(galaxy_feature_vector_export_path, features, delimiter=",")
print("File saved in directory " + galaxy_feature_vector_export_path)